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Statistical distributions commonly used in measurement uncertainty in laboratory medicine.

ABSTRACT: Uncertainty is an inseparable part of all types of measurement. Recently, the International Organization for Standardization (ISO) released a new standard (ISO 20914) on how to calculate measurement uncertainty (MU) in laboratory medicine. This standard can be regarded as the beginning of a new era in laboratory medicine. Measurement uncertainty comprises various components and is used to calculate the total uncertainty. All components must be expressed in standard deviation (SD) and then combined. However, the characteristics of these components are not the same; some are expressed as SD, while others are expressed as a ± b, such as the purity of the reagents. All non-SD variables must be transformed into SD, which requires a detailed knowledge of common statistical distributions used in the calculation of MU. Here, the main statistical distributions used in MU calculation are briefly summarized.

SUBMITTER: Coskun A

PROVIDER: S-EPMC6999182 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Statistical distributions commonly used in measurement uncertainty in laboratory medicine.

Coskun Abdurrahman A Oosterhuis Wytze P WP

Biochemia medica 20200201 1

Uncertainty is an inseparable part of all types of measurement. Recently, the International Organization for Standardization (ISO) released a new standard (ISO 20914) on how to calculate measurement uncertainty (MU) in laboratory medicine. This standard can be regarded as the beginning of a new era in laboratory medicine. Measurement uncertainty comprises various components and is used to calculate the total uncertainty. All components must be expressed in standard deviation (SD) and then combin ...[more]

PMID: 32063728

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Project description:BackgroundSpurious associations between single nucleotide polymorphisms and phenotypes are a major issue in genome-wide association studies and have led to underestimation of type 1 error rate and overestimation of the number of quantitative trait loci found. Many authors have investigated the influence of population structure on the robustness of methods by simulation. This paper is aimed at developing further the algebraic formalization of power and type 1 error rate for some of the classical statistical methods used: simple regression, two approximate methods of mixed models involving the effect of a single nucleotide polymorphism (SNP) and a random polygenic effect (GRAMMAR and FASTA) and the transmission/disequilibrium test for quantitative traits and nuclear families. Analytical formulae were derived using matrix algebra for the first and second moments of the statistical tests, assuming a true mixed model with a polygenic effect and SNP effects.ResultsThe expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods.ConclusionsThe formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.

Project description:BackgroundNational and international primary CVD risk screening guidelines focus on using total CVD risk scores. Recently, we developed a non-laboratory-based CVD risk score (inputs: age, sex, smoking, diabetes, systolic blood pressure, treatment of hypertension, body-mass index), which can assess risk faster and at lower costs compared to laboratory-based scores (inputs include cholesterol values). We aimed to assess the exchangeability of the non-laboratory-based risk score to four commonly used laboratory-based scores (Framingham CVD [2008, 1991 versions], and Systematic COronary Risk Evaluation [SCORE] for low and high risk settings) in an external validation population.Methods and findingsAnalyses were based on individual-level, score-specific rankings of risk for adults in the Third National Health and Nutrition Examination Survey (NHANES III) aged 25-74 years, without history of CVD or cancer (n?=?5,999). Risk characterization agreement was based on overlap in dichotomous risk characterization (thresholds of 10-year risk >10-20%) and Spearman rank correlation. Risk discrimination was assessed using receiver operator characteristic curve analysis (10-year CVD death outcome). Risk characterization agreement ranged from 91.9-95.7% and 94.2-95.1% with Spearman correlation ranges of 0.957-0.980 and 0.946-0.970 for men and women, respectively. In men, c-statistics for the non-laboratory-based, Framingham (2008, 1991), and SCORE (high, low) functions were 0.782, 0.776, 0.781, 0.785, and 0.785, with p-values for differences relative to the non-laboratory-based score of 0.44, 0.89, 0.68 and 0.65, respectively. In women, the corresponding c-statistics were 0.809, 0.834, 0.821, 0.792, and 0.792, with corresponding p-values of 0.04, 0.34, 0.11 and 0.09, respectively.ConclusionsEvery score discriminated risk of CVD death well, and there was high agreement in risk characterization between non-laboratory-based and laboratory-based risk scores, which suggests that the non-laboratory-based score can be a useful proxy for Framingham or SCORE functions in resource-limited settings. Future external validation studies can assess whether the sex-specific risk discrimination results hold in other populations.

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Statistical distributions commonly used in measurement uncertainty in laboratory medicine.

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Statistical distributions commonly used in measurement uncertainty in laboratory medicine.

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